Deep Learning

Deep Learning has become a popular area, with deep neural network methods obtaining obtaining state-of-the-art results on applications in computer vision, natural language processing, and reinforcement learning. This course is well rounded in terms of concepts and helps you understand the fundamentals of Deep Learning. The course starts off gradually with supervised learning and it progresses into the more complicated concepts such as Variational Auto Encoders and Generative Adversarial Networks. We get a complete hands on with Tensorflow/Keras which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models (if you attend the lab sessions!).

Administrative Details

Here you will find administrative information for the Winter 1398.

Instructor: Dr. Mohsen Afsharchi, afsharchi at

Teaching Assistants: Meysam Azad, m.azadm at , Sudeh Behrouzinia, behrouzinia at

Lectures: 8-10 Sat and 10-12 Tue

Prerequisites: Clear understanding of calculus, vector algebra and probability.


The main books for:

  1. “Deep Learning”  By Goodfellow, Ian, Yoshua Bengio, and Aaron Courville, 2016.

  2. “Deep Learning with Python”  By François Chollet, 2018.

Lecture materials will be drawn from the recent online materials and is mainly based on lectures of Dr. Andrew Ng

Course Syllabus and Lecture Notes

  1. Introduction to Deep Learning (Introduction & History) (Overview of Deep Learning Applications)
    A brief history of artificial neural networks and Deep Learning
    Reading Materials:
    Deep Learning most amazing applications
  2. Machine Learning Recap (The Learning Process) (Logistic Regression with a Neural Network Mindset)
    Logistic Regression, Optimization and Gradient Descent
    Reading Materials:
    Logistic Regression

    Binary Classification notes
  3. Neural Networks (Shallow Neural Networks) (Deep Neural Networks)
    Reading Materials:
    Neural Networks Notes 1

    Neural Networks Notes 2
    Using Neural Nets to Recognize Handwritten Digits
  4. Introduction to ConvNets (Convolution) (Padding, Strided Convolution, Pooling and Convolution over Volume)
    Reading Materials:
    ConvNets: A toy example
    (Brandon Rohrer)
    ConvNet Notes
  5. Classic Networks (LeNet, AlexNet, VGG, ResNet, Inception)
    Reading Materials:
    LeNet Gradient-Based Learning Applied to Document
    ImageNet Classification with Deep Convolutional Neural Networks
    VGG-16 Very Deep Convolutional Networks for Large-Scale Image Recognition
    ResNet Deep Residual Learning for Image Recognition
    Going deeper with convolutions
    Taste of Code: SimpleConvNet , Resnet
  6. A Brief Introduction to Computer Vision (Object Localization) (Multiple Object Detection)
    Reading Materials:
    You Look Only Once: Unified, Real-Time Object Detection

    OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
  7. Improving Deep Neural Networks (Regularization) (Batch Normalization) (Weight Initialization) (Demo)
    Reading Materials:
    Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
    Initializing Neural Networks
    Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
    Understanding the difficulty of training deep feedforward neural networks
  8. Optimization Algorithms (MiniBatch SGD, Gradient descent with momentum, AdaGrad, RMSProp, ADAM)
    Reading Materials:
    10 Gradient Descent Optimization Algorithms
    Optimization Algorithms : Supplementary Code
  9. Sequence Models (Recurrent Neural Networks)(Gated Recurrent Unit and LSTM)
    Reading Materials:
    Recurrent Neural Networks Tutorial
    Backpropagation Through Time and Vanishing Gradients
  10. Word Embedding (Word2Vec)
    Reading Materials:
    Demystifying Neural Network in Skip-Gram Language Modeling
    Optimize Computational Efficiency of Skip-Gram with Negative Sampling
    The backpropagation algorithm for Word2Vec
  11. Deep NLP Applications (Language modelling, Machine Translation)
    Reading Materials:
    Sequence to Sequence Learning with Neural Networks
    Recurrent neural network based language model
  12. Semi-supervised Learning (AutoEncoders) (Variational AutoEncoders)
    Reading Materials:
    Variational Autoencoder: Intuition and Implementation
    What is a variational autoencoder?
  13. Generative Adversarial Networks (GAN)
    Reading Materials:
    Only Numpy: Implementing GAN with Numpy
    How to Develop a 1D Generative Adversarial Network From Scratch in Keras

Course Work and Evaluation

Students will be evaluated in this class through a series of exams, an in-class presentation and a semester project. The semester project will involve implementing a non-trivial  program that has been designed using knowledge and techniques covered in class. In-class students will work in teams to work on the project.